{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-06d4c2bb03b3>:14: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  dateparse = lambda dates: pd.datetime.strptime(dates, '%Y%m%d')\n",
      "<ipython-input-1-06d4c2bb03b3>:17: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  user_balance = user_balance.groupby(['report_date'])['share_amt', 'total_purchase_amt'].sum()\n",
      "<ipython-input-1-06d4c2bb03b3>:125: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m-%d')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\python\\python38\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:1659: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From <ipython-input-1-06d4c2bb03b3>:144: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From <ipython-input-1-06d4c2bb03b3>:149: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From <ipython-input-1-06d4c2bb03b3>:155: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From <ipython-input-1-06d4c2bb03b3>:159: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "WARNING:tensorflow:From c:\\python\\python38\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py:962: Layer.add_variable (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "WARNING:tensorflow:From c:\\python\\python38\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py:970: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "epoch: 1 fit loss: 0.17458461 valid loss: 0.13438272\n",
      "epoch: 2 fit loss: 0.063646294 valid loss: 0.026913527\n",
      "epoch: 3 fit loss: 0.054695085 valid loss: 0.0248337\n",
      "epoch: 4 fit loss: 0.046958283 valid loss: 0.022783345\n",
      "epoch: 5 fit loss: 0.04261311 valid loss: 0.021246012\n",
      "epoch: 6 fit loss: 0.040328667 valid loss: 0.020310892\n",
      "epoch: 7 fit loss: 0.03949595 valid loss: 0.019523546\n",
      "epoch: 8 fit loss: 0.038982954 valid loss: 0.01934467\n",
      "epoch: 9 fit loss: 0.037150145 valid loss: 0.018240469\n",
      "epoch: 10 fit loss: 0.036165062 valid loss: 0.0181107\n",
      "epoch: 11 fit loss: 0.036063325 valid loss: 0.018013496\n",
      "epoch: 12 fit loss: 0.035573777 valid loss: 0.017781772\n",
      "epoch: 13 fit loss: 0.035578184 valid loss: 0.017367456\n",
      "epoch: 14 fit loss: 0.034882136 valid loss: 0.017294556\n",
      "epoch: 15 fit loss: 0.034384653 valid loss: 0.01674654\n",
      "epoch: 16 fit loss: 0.034386933 valid loss: 0.01691702\n",
      "epoch: 17 fit loss: 0.034043208 valid loss: 0.016505321\n",
      "epoch: 18 fit loss: 0.034450453 valid loss: 0.016453125\n",
      "epoch: 19 fit loss: 0.03375868 valid loss: 0.015982088\n",
      "epoch: 20 fit loss: 0.03342469 valid loss: 0.016090792\n",
      "epoch: 21 fit loss: 0.033477794 valid loss: 0.01597071\n",
      "epoch: 22 fit loss: 0.033261493 valid loss: 0.015826797\n",
      "epoch: 23 fit loss: 0.032861102 valid loss: 0.01566476\n",
      "epoch: 24 fit loss: 0.032815352 valid loss: 0.015928632\n",
      "epoch: 25 fit loss: 0.03258897 valid loss: 0.015957888\n",
      "epoch: 26 fit loss: 0.03246699 valid loss: 0.015698224\n",
      "epoch: 27 fit loss: 0.03300043 valid loss: 0.01616052\n",
      "epoch: 28 fit loss: 0.03191772 valid loss: 0.016038066\n",
      "epoch: 29 fit loss: 0.03232716 valid loss: 0.016286774\n",
      "epoch: 30 fit loss: 0.03174093 valid loss: 0.016479623\n",
      "epoch: 31 fit loss: 0.032130495 valid loss: 0.016365333\n",
      "epoch: 32 fit loss: 0.03190965 valid loss: 0.016549736\n",
      "epoch: 33 fit loss: 0.031711422 valid loss: 0.016668076\n",
      "epoch: 34 fit loss: 0.031851716 valid loss: 0.01707172\n",
      "epoch: 35 fit loss: 0.03145087 valid loss: 0.017377067\n",
      "epoch: 36 fit loss: 0.03140596 valid loss: 0.017712597\n",
      "epoch: 37 fit loss: 0.03159675 valid loss: 0.017689887\n",
      "epoch: 38 fit loss: 0.031787544 valid loss: 0.018546347\n",
      "epoch: 39 fit loss: 0.03119415 valid loss: 0.018751984\n",
      "epoch: 40 fit loss: 0.031210033 valid loss: 0.019101495\n",
      "epoch: 41 fit loss: 0.03086697 valid loss: 0.019100798\n",
      "epoch: 42 fit loss: 0.031096319 valid loss: 0.01910741\n",
      "epoch: 43 fit loss: 0.03102034 valid loss: 0.019024557\n",
      "epoch: 44 fit loss: 0.030641811 valid loss: 0.01908284\n",
      "epoch: 45 fit loss: 0.030386494 valid loss: 0.019683877\n",
      "epoch: 46 fit loss: 0.029990535 valid loss: 0.019246252\n",
      "epoch: 47 fit loss: 0.029658211 valid loss: 0.019075956\n",
      "epoch: 48 fit loss: 0.029823923 valid loss: 0.01889476\n",
      "epoch: 49 fit loss: 0.029294962 valid loss: 0.019025428\n",
      "epoch: 50 fit loss: 0.028944742 valid loss: 0.019443009\n",
      "epoch: 51 fit loss: 0.02894232 valid loss: 0.019036435\n",
      "epoch: 52 fit loss: 0.028694736 valid loss: 0.018990688\n",
      "epoch: 53 fit loss: 0.028781867 valid loss: 0.019410051\n",
      "epoch: 54 fit loss: 0.02887426 valid loss: 0.019247962\n",
      "epoch: 55 fit loss: 0.028432107 valid loss: 0.019162249\n",
      "epoch: 56 fit loss: 0.028636377 valid loss: 0.018615281\n",
      "epoch: 57 fit loss: 0.028256508 valid loss: 0.018726464\n",
      "epoch: 58 fit loss: 0.028281828 valid loss: 0.019115794\n",
      "epoch: 59 fit loss: 0.028255967 valid loss: 0.019026864\n",
      "epoch: 60 fit loss: 0.027893163 valid loss: 0.019061208\n",
      "epoch: 61 fit loss: 0.027990008 valid loss: 0.019054705\n",
      "epoch: 62 fit loss: 0.027675519 valid loss: 0.01874335\n",
      "epoch: 63 fit loss: 0.027557798 valid loss: 0.01917687\n",
      "epoch: 64 fit loss: 0.027643353 valid loss: 0.018650299\n",
      "epoch: 65 fit loss: 0.027283547 valid loss: 0.018750872\n",
      "epoch: 66 fit loss: 0.026972853 valid loss: 0.017866258\n",
      "best epoch:  66\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1296x864 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow\n",
    "tf = tensorflow.compat.v1\n",
    "# 关闭动态图执行\n",
    "tf.disable_eager_execution()\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "def generate_data():\n",
    "    dateparse = lambda dates: pd.datetime.strptime(dates, '%Y%m%d')\n",
    "    user_balance = pd.read_csv('user_balance_table.csv', parse_dates=['report_date'], date_parser=dateparse)\n",
    "\n",
    "    user_balance = user_balance.groupby(['report_date'])['share_amt', 'total_purchase_amt'].sum()\n",
    "    user_balance.reset_index(inplace=True)\n",
    "    user_balance.index = user_balance['report_date']\n",
    "\n",
    "    user_balance = user_balance['2014-03-01':'2014-08-31']\n",
    "\n",
    "    data = {'total_purchase_amt': user_balance['total_purchase_amt']}\n",
    "\n",
    "    df = pd.DataFrame(data=data, index=user_balance.index)\n",
    "    df.to_csv(path_or_buf='single_purchase_seq.csv')\n",
    "\n",
    "\n",
    "# 数据集归一化\n",
    "def get_normal_data(purchase_seq):\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    scaled_data = scaler.fit_transform(purchase_seq[['total_purchase_amt']])\n",
    "    scaled_x_data = scaled_data[0: -1]\n",
    "    scaled_y_data = scaled_data[1:]\n",
    "    return scaled_x_data, scaled_y_data, scaler\n",
    "\n",
    "\n",
    "# 构造训练集\n",
    "def get_train_data(scaled_x_data, scaled_y_data, divide_train_valid_index, time_step):\n",
    "    train_x, train_y = [], []\n",
    "    normalized_train_feature = scaled_x_data[0: -divide_train_valid_index]\n",
    "    normalized_train_label = scaled_y_data[0: -divide_train_valid_index]\n",
    "    for i in range(len(normalized_train_feature) - time_step + 1):\n",
    "        train_x.append(normalized_train_feature[i:i + time_step].tolist())\n",
    "        train_y.append(normalized_train_label[i:i + time_step].tolist())\n",
    "    return train_x, train_y\n",
    "\n",
    "\n",
    "# 构造拟合训练集\n",
    "def get_train_fit_data(scaled_x_data, scaled_y_data, divide_train_valid_index, time_step):\n",
    "    train_fit_x, train_fit_y = [], []\n",
    "    normalized_train_feature = scaled_x_data[0: -divide_train_valid_index]\n",
    "    normalized_train_label = scaled_y_data[0: -divide_train_valid_index]\n",
    "    train_fit_remain = len(normalized_train_label) % time_step\n",
    "    train_fit_num = int((len(normalized_train_label) - train_fit_remain) / time_step)\n",
    "    temp = []\n",
    "    for i in range(train_fit_num):\n",
    "        train_fit_x.append(normalized_train_feature[i * time_step:(i + 1) * time_step].tolist())\n",
    "        temp.extend(normalized_train_label[i * time_step:(i + 1) * time_step].tolist())\n",
    "    if train_fit_remain > 0:\n",
    "        train_fit_x.append(normalized_train_feature[-time_step:].tolist())\n",
    "        temp.extend(normalized_train_label[-train_fit_remain:].tolist())\n",
    "    for i in temp:\n",
    "        train_fit_y.append(i[0])\n",
    "    return train_fit_x, train_fit_y, train_fit_remain\n",
    "\n",
    "\n",
    "# 构造验证集\n",
    "def get_valid_data(scaled_x_data, scaled_y_data, divide_train_valid_index, divide_valid_test_index, time_step):\n",
    "    valid_x, valid_y = [], []\n",
    "    normalized_valid_feature = scaled_x_data[-divide_train_valid_index: -divide_valid_test_index]\n",
    "    normalized_valid_label = scaled_y_data[-divide_train_valid_index: -divide_valid_test_index]\n",
    "    valid_remain = len(normalized_valid_label) % time_step\n",
    "    valid_num = int((len(normalized_valid_label) - valid_remain) / time_step)\n",
    "    temp = []\n",
    "    for i in range(valid_num):\n",
    "        valid_x.append(normalized_valid_feature[i * time_step:(i + 1) * time_step].tolist())\n",
    "        temp.extend(normalized_valid_label[i * time_step:(i + 1) * time_step].tolist())\n",
    "    if valid_remain > 0:\n",
    "        valid_x.append(normalized_valid_feature[-time_step:].tolist())\n",
    "        temp.extend(normalized_valid_label[-valid_remain:].tolist())\n",
    "    for i in temp:\n",
    "        valid_y.append(i[0])\n",
    "    return valid_x, valid_y, valid_remain\n",
    "\n",
    "\n",
    "# 构造测试集\n",
    "def get_test_data(scaled_x_data, scaled_y_data, divide_valid_test_index, time_step):\n",
    "    test_x, test_y = [], []\n",
    "    normalized_test_feature = scaled_x_data[-divide_valid_test_index:]\n",
    "    normalized_test_label = scaled_y_data[-divide_valid_test_index:]\n",
    "    test_remain = len(normalized_test_label) % time_step\n",
    "    test_num = int((len(normalized_test_label) - test_remain) / time_step)\n",
    "    temp = []\n",
    "    for i in range(test_num):\n",
    "        test_x.append(normalized_test_feature[i * time_step:(i + 1) * time_step].tolist())\n",
    "        temp.extend(normalized_test_label[i * time_step:(i + 1) * time_step].tolist())\n",
    "    if test_remain > 0:\n",
    "        test_x.append(scaled_x_data[-time_step:].tolist())\n",
    "        temp.extend(normalized_test_label[-test_remain:].tolist())\n",
    "    for i in temp:\n",
    "        test_y.append(i[0])\n",
    "    return test_x, test_y, test_remain\n",
    "\n",
    "generate_data()\n",
    "\n",
    "\n",
    "# 模型参数\n",
    "lr = 1e-3  # 学习率\n",
    "batch_size = 10  # minibatch 大小\n",
    "rnn_unit = 30  # LSTM 隐藏层神经元数量\n",
    "input_size = 1  # 单元的输入数量\n",
    "output_size = 1  # 单元的输出数量\n",
    "time_step = 15  # 时间长度\n",
    "epochs = 1000  # 训练次数\n",
    "gradient_threshold = 15  # 梯度裁剪阈值\n",
    "stop_loss = np.float32(0.045)  # 训练停止条件。当训练误差 + 验证误差小于阈值时，停止训练\n",
    "train_keep_prob = [1.0, 0.5, 1.0]  # 训练时 dropout 神经元保留比率\n",
    "\n",
    "# 数据切分参数\n",
    "divide_train_valid_index = 30\n",
    "divide_valid_test_index = 10\n",
    "\n",
    "# 数据准备\n",
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m-%d')\n",
    "single_purchase_seq = pd.read_csv('single_purchase_seq.csv', parse_dates=['report_date'], index_col='report_date', date_parser=dateparse)\n",
    "\n",
    "scaled_x_data, scaled_y_data, scaler = get_normal_data(single_purchase_seq)\n",
    "train_x, train_y = get_train_data(scaled_x_data, scaled_y_data, divide_train_valid_index, time_step)\n",
    "train_fit_x, train_fit_y, train_fit_remain = get_train_fit_data(scaled_x_data, scaled_y_data, divide_train_valid_index, time_step)\n",
    "valid_x, valid_y, valid_remain = get_valid_data(scaled_x_data, scaled_y_data, divide_train_valid_index, divide_valid_test_index, time_step)\n",
    "test_x, test_y, test_remain = get_test_data(scaled_x_data, scaled_y_data, divide_valid_test_index, time_step)\n",
    "\n",
    "\n",
    "def lstm(X, keep_prob):\n",
    "    batch_size = tf.shape(X)[0]  # minibatch 大小\n",
    "\n",
    "    # 输入到 LSTM 输入的转换，一层全连接的网络，其中权重初始化采用截断的高斯分布，激活函数采用tanh\n",
    "    weights = tf.Variable(tf.truncated_normal(shape=[input_size, rnn_unit]))\n",
    "    biases = tf.Variable(tf.constant(0.1, shape=[rnn_unit, ]))\n",
    "    input = tf.reshape(X, [-1, input_size])\n",
    "\n",
    "    tanh_layer = tf.nn.tanh(tf.matmul(input, weights) + biases)\n",
    "    input_rnn = tf.nn.dropout(tanh_layer, keep_prob[0])\n",
    "    input_rnn = tf.reshape(input_rnn, [-1, time_step, rnn_unit])\n",
    "\n",
    "    # 两层 LSTM 网络，激活函数默认采用 tanh，当网络层数较深时，建议使用 relu\n",
    "    initializer = tf.truncated_normal_initializer()\n",
    "    cell_1 = tf.nn.rnn_cell.LSTMCell(forget_bias=1.0, num_units=rnn_unit, use_peepholes=True, num_proj=None, initializer=initializer, name='lstm_cell_1')\n",
    "    cell_1_drop = tf.nn.rnn_cell.DropoutWrapper(cell=cell_1, output_keep_prob=keep_prob[1])\n",
    "\n",
    "    cell_2 = tf.nn.rnn_cell.LSTMCell(forget_bias=1.0, num_units=rnn_unit, use_peepholes=True, num_proj=output_size, initializer=initializer, name='lstm_cell_2')\n",
    "    cell_2_drop = tf.nn.rnn_cell.DropoutWrapper(cell=cell_2, output_keep_prob=keep_prob[2])\n",
    "\n",
    "    mutilstm_cell = tf.nn.rnn_cell.MultiRNNCell(cells=[cell_1_drop, cell_2_drop], state_is_tuple=True)\n",
    "    init_state = mutilstm_cell.zero_state(batch_size, dtype=tf.float32)\n",
    "\n",
    "    with tf.variable_scope('lstm', reuse=tf.AUTO_REUSE):\n",
    "        output, state = tf.nn.dynamic_rnn(cell=mutilstm_cell, inputs=input_rnn, initial_state=init_state, dtype=tf.float32)\n",
    "\n",
    "    return output, state\n",
    "\n",
    "# 获取拟合数据，这里用于拟合，关闭 dropout\n",
    "def get_fit_seq(x, remain, sess, output, X, keep_prob, scaler, inverse):\n",
    "    fit_seq = []\n",
    "    if inverse:\n",
    "        # 前面对数据进行了归一化，这里反归一化还原数据\n",
    "        temp = []\n",
    "        for i in range(len(x)):\n",
    "            next_seq = sess.run(output, feed_dict={X: [x[i]], keep_prob: [1.0, 1.0, 1.0]})\n",
    "            if i == len(x) - 1:\n",
    "                temp.extend(scaler.inverse_transform(next_seq[0].reshape(-1, 1))[-remain:])\n",
    "            else:\n",
    "                temp.extend(scaler.inverse_transform(next_seq[0].reshape(-1, 1)))\n",
    "        for i in temp:\n",
    "            fit_seq.append(i[0])\n",
    "    else:\n",
    "        for i in range(len(x)):\n",
    "            next_seq = sess.run(output,\n",
    "                                feed_dict={X: [x[i]], keep_prob: [1.0, 1.0, 1.0]})\n",
    "            if i == len(x) - 1:\n",
    "                fit_seq.extend(next_seq[0].reshape(1, -1).tolist()[0][-remain:])\n",
    "            else:\n",
    "                fit_seq.extend(next_seq[0].reshape(1, -1).tolist()[0])\n",
    "\n",
    "    return fit_seq\n",
    "\n",
    "\n",
    "def train_lstm():\n",
    "    X = tf.placeholder(tf.float32, [None, time_step, input_size])\n",
    "    Y = tf.placeholder(tf.float32, [None, time_step, output_size])\n",
    "\n",
    "    keep_prob = tf.placeholder(tf.float32, [None])\n",
    "    output, state = lstm(X, keep_prob)\n",
    "    loss = tf.losses.mean_squared_error(tf.reshape(output, [-1]), tf.reshape(Y, [-1]))\n",
    "\n",
    "    # 梯度优化与裁剪\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=lr)\n",
    "    grads, variables = zip(*optimizer.compute_gradients(loss))\n",
    "    grads, global_norm = tf.clip_by_global_norm(grads, gradient_threshold)\n",
    "    train_op = optimizer.apply_gradients(zip(grads, variables))\n",
    "\n",
    "    X_train_fit = tf.placeholder(tf.float32, [None])\n",
    "    Y_train_fit = tf.placeholder(tf.float32, [None])\n",
    "    train_fit_loss = tf.losses.mean_squared_error(tf.reshape(X_train_fit, [-1]), tf.reshape(Y_train_fit, [-1]))\n",
    "\n",
    "    X_valid = tf.placeholder(tf.float32, [None])\n",
    "    Y_valid = tf.placeholder(tf.float32, [None])\n",
    "    valid_fit_loss = tf.losses.mean_squared_error(tf.reshape(X_valid, [-1]), tf.reshape(Y_valid, [-1]))\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        fit_loss_seq = []\n",
    "        valid_loss_seq = []\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "            for index in range(len(train_x) - batch_size + 1):\n",
    "               sess.run(train_op, feed_dict={X: train_x[index: index + batch_size], Y: train_y[index: index + batch_size], keep_prob: train_keep_prob})\n",
    "\n",
    "            # 拟合训练集和验证集\n",
    "            train_fit_seq = get_fit_seq(train_fit_x, train_fit_remain, sess, output, X, keep_prob, scaler, False)\n",
    "            train_loss = sess.run(train_fit_loss, {X_train_fit: train_fit_seq, Y_train_fit: train_fit_y})\n",
    "            fit_loss_seq.append(train_loss)\n",
    "\n",
    "            valid_seq = get_fit_seq(valid_x, valid_remain, sess, output, X, keep_prob, scaler, False)\n",
    "            valid_loss = sess.run(valid_fit_loss, {X_valid: valid_seq, Y_valid: valid_y})\n",
    "            valid_loss_seq.append(valid_loss)\n",
    "\n",
    "            print('epoch:', epoch + 1, 'fit loss:', train_loss, 'valid loss:', valid_loss)\n",
    "\n",
    "            # 提前终止条件。\n",
    "            # 常见的方法是验证集达到最小值，再往后训练 n 步，loss 不再减小，实际测试这里使用的效果不好。\n",
    "            # 这里选择 stop_loss 是经过多次尝试得到的阈值。\n",
    "            if train_loss + valid_loss <= stop_loss:\n",
    "                train_fit_seq = get_fit_seq(train_fit_x, train_fit_remain, sess, output, X, keep_prob, scaler, True)\n",
    "                valid_fit_seq = get_fit_seq(valid_x, valid_remain, sess, output, X, keep_prob, scaler, True)\n",
    "                test_fit_seq = get_fit_seq(test_x, test_remain, sess, output, X, keep_prob, scaler, True)\n",
    "                print('best epoch: ', epoch + 1)\n",
    "                break\n",
    "\n",
    "\n",
    "    return fit_loss_seq, valid_loss_seq, train_fit_seq, valid_fit_seq, test_fit_seq\n",
    "\n",
    "\n",
    "fit_loss_seq, valid_loss_seq, train_fit_seq, valid_fit_seq, test_fit_seq = train_lstm()\n",
    "\n",
    "# 切分训练集、测试集\n",
    "purchase_seq_train = single_purchase_seq[1:-divide_train_valid_index]\n",
    "purchase_seq_valid = single_purchase_seq[-divide_train_valid_index:-divide_valid_test_index]\n",
    "purchase_seq_test = single_purchase_seq[-divide_valid_test_index:]\n",
    "\n",
    "plt.figure(figsize=(18, 12))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.title('loss')\n",
    "plt.plot(fit_loss_seq, label='fit_loss', color='blue')\n",
    "plt.plot(valid_loss_seq, label='valid_loss', color='red')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.title('train')\n",
    "seq_train_fit = pd.DataFrame(columns=['total_purchase_amt'], data=train_fit_seq, index=purchase_seq_train.index)\n",
    "plt.plot(purchase_seq_train['total_purchase_amt'], label='value', color='blue')\n",
    "plt.plot(seq_train_fit['total_purchase_amt'], label='fit_value', color='red')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.title('valid')\n",
    "seq_valid_fit = pd.DataFrame(columns=['total_purchase_amt'], data=valid_fit_seq, index=purchase_seq_valid.index)\n",
    "plt.plot(purchase_seq_valid['total_purchase_amt'], label='value', color='blue')\n",
    "plt.plot(seq_valid_fit['total_purchase_amt'], label='fit_value', color='red')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.title('test')\n",
    "seq_test_fit = pd.DataFrame(columns=['total_purchase_amt'], data=test_fit_seq, index=purchase_seq_test.index)\n",
    "plt.plot(purchase_seq_test['total_purchase_amt'], label='value', color='blue')\n",
    "plt.plot(seq_test_fit['total_purchase_amt'], label='fit_value', color='red')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.show()\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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